Assessing the Preparedness of Information Institutions For the Knowledge-Based Economy
Bibliographic record
Abstract
Organizations, companies, and workers are continuously urged to prepare for the new competitive era, called Knowledge-Based Economy. Effective exploitation of information in organizations appears to be a distinguishing characteristic of this new socioeconomic model. Case studies were carried out in two information institutions in Singapore to assess their preparedness for the Knowledge-Based Economy. Performance indicators were identified in six areas based on the innovative products and services designed and delivered by leading information institutions. The commonly identified new activities that appeared to have been initiated in response to the new economy were listed as parameters that can help assess the preparation of an information institution for its role in the KBE. The study found that the two information institutions used for case studies were quite conscious to play their role in the new economy. They introduced more flexible organizational structures and initiated new services and products to respond to the requirements of the new economy. They also took steps to expand the roles of their information workers and established strategic alliances to strengthen internal communication and external relations. They put in place mechanisms to seek user feedback and made a creative use of outsourcing to allow more time to information professionals to concentrate on value added services. Our experience suggests that once converted into a checklist, the proposed performance indicators can be successfully used to assess preparedness of other information institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.034 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".